In previous optimization-based methods of 3D planar-faced object reconstruction from single 2D line drawings, the missing depths of the vertices of a line drawing (and other parameters in some methods) are used as the variables of the objective functions. A 3D object with planar faces is derived by finding values for these variables that minimize the objective functions. These methods work well for simple objects with a small number TV of variables. As N grows, however, it is very difficult for them to find the expected objects. This is because with the nonlinear objective functions in a space of large dimension , the search for optimal solutions can easily get trapped into local minima. In this paper, we use the parameters of the planes that pass through the planar faces of an object as the variables of the objective function. This leads to a set of linear constraints on the planes of the object, resulting in a much lower dimensional null space where optimization is easier to achieve. We prove that the dimension of this null space is exactly equal to the minimum number of vertex depths that define the 3D object. Since a practical line drawing is usually not an exact projection of a 3D object, we expand the null space to a larger space based on the singular value decomposition of the projection matrix of the line drawing. In this space, robust 3D reconstruction can be achieved. Compared with the two most related methods, our method not only can reconstruct more complex 3D objects from 2D line drawings but also is computationally more efficient.